Socio-economic data on grid level (Wave 6). House type

https://doi.org/10.7807/microm:haustyp:v6

1

Dataset

• RWI
• microm

• RWI-GEO-GRID

2018-08-13

Essen

• Budde, Rüdiger (RWI – Leibniz-Institut für Wirtschaftsforschung) (Contact Person)
• Eilers, Lea (RWI – Leibniz-Institut für Wirtschaftsforschung) (Researcher)
• microm Micromarketing-Systeme und Consult GmbH (Data Collector)
• RWI – Leibniz-Institut für Wirtschaftsforschung (Editor)

German

• JEL:
  • Urban, Rural, Regional, and Transportation Analysis, Housing, Infrastructure (O18)
  • Regional Labor Markets, Population, Neighborhood Characteristics (R23)

house type; family home; multi-storey building; block of flats; apartment block; commercial use; raster data; RWI-GEO-Grid

• Abstract

  The variable house type indicates the size of a house and is based on the sum of the households and the number of firms in a house. Houses with particularly many commercial addresses are categorised as commercially used houses. Single- and two-family homes are distinguished according whether the house type in that road (section) is homogenous or not. There are 7 house types in the data:  single- and two-family homes in homogenous road sections, single- and two-family homes in heterogeneous road sections, 3-5 family homes, 6-9 family homes, blocks of flats with 10-19 households, multi-storey buildings with 20 and more households, mainly commercially used houses. The house type allows for conclusions on geographical information, such as the location in an urban area: while cities predominantly consist of multi-family houses, the countryside is shaped by single- and two-family homes (microm 2016, p. 32).

• 2005
• 2009
• 2010
• 2011
• 2012
• 2013
• 2014
• 2015

• Germany (DE)

Microm uses more than a billion individual data points for the aggregation of the microm dataset. These are anonymised and stem from various data sources. The data points are available for all 40.9 million households in Germany, while the final data product contains information on approximately 20 million houses (microm 2016, p. 8).

• Cross-section

• Other

  For data privacy reasons, houses within a residential environment are summed up to a "virtual" micro-geographic segment (so-called micro-cell), which on average comprises eight, but at least five households. Houses in which at least five households live become a distinct micro-cell, while houses with less than five households are combined with similar houses on the same street. Combined houses are as close as possible in spatial terms. Structural indicators are aggregated on the micro-cell level and subsequently computed household level averages are computed (microm 2016, p.8). If such data exist, the calculated data is made consistent with official data sources (microm 2014, p. 2). Additionally,  due to the cooperation with SOEP, it is possible to validate the small-scale regional data of microm (microm 2016, p. 8). The dataset is based on the variable group microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (incl. those purely used for business), and number of residential houses (excl. those purely used for business) (cf. microm 2016, p. 26). The number of houses on the street segment level is the basis for all aggregations to other regional levels. Based on business registers, the number of enterprises in each house is determined.

• Unit Type: Geographic Unit
  Number of Units: 1515276
  Number of Variables: 13
  Type of Data: Microdata
  
  • File Name: 107807_microm_haustyp_V6.dta"
    File Format: Stata
    File Size: 126915 KB
    

Besides information on the house type the dataset contains the geographical key of the raster point as well as the variables of the group microm-Basis. These variables are data provided for scientific use by the FDZ Ruhr at RWI. Data on such a small regional scale (1km²) is not collected directly for all parts of Germany, which makes this dataset a valuable addition for small-scale regional analyses. A basic description on the data collection of the individual variables is found in the microm handbook (microm 2016). Details on the data generation are not publicly available, however the procedure of collecting particular data is known (cf. procedure of data collection). Screenings of the FDZ Ruhr do not indicate issues with data quality.

On-site

microm Micromarketing-Systeme und Consult GmbH

• Continues
  DOI: 10.7807/microm:haustyp:v5 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:kinder:v6 (Dataset)
  
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  DOI: 10.7807/microm:pkwseg:suf:v6:1 (Dataset)
  
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  DOI: 10.7807/microm:kaufkraft:v6 (Dataset)
  
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  DOI: 10.7807/microm:pkwmarken:suf:v6:1 (Dataset)
  
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  DOI: 10.7807/microm:auslaender:v6 (Dataset)
  
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  DOI: 10.7807/microm:hstruktur:v6 (Dataset)
  
• Is continued by
  DOI: 10.7807/microm:alq:v6 (Dataset)
  
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  DOI: 10.7807/microm:einwohner:v6 (Dataset)
  
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  DOI: 10.7807/microm:einwGeAl:v6 (Dataset)
  
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  DOI: 10.7807/microm:ethno:v6 (Dataset)
  
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  DOI: 10.7807/microm:zahlindex:v6 (Dataset)
  

• Budde, R.; Eilers, L. (2014): Sozioökonomische Daten auf Rasterebene – Datenbeschreibung der microm-Rasterdaten. RWI Materialien 077. Essen: RWI.

  • ID: http://hdl.handle.net/10419/97627 (Handle)

• Bauer, T. K.; Budde, R.; Micheli, M.; Neumann, U. (2015): Immobilienmarkteffekte des Emscherumbaus? Raumforschung und Raumordnung 73(4): 269-283.

  • ID: 10.1007/s13147-015-0356-5 (DOI)

• Hentschker, C., and A. Wübker (2016). The impact of technology diffusion in health care markets: Evidence from heart attack treatment. Ruhr Economic Papers #632. Essen: RWI.

  • ID: 10.4419/86788734 (DOI)

• microm Consumer Marketing (2016), Datenhandbuch: Arbeitsunterlagen für microm MARKET & GEO. Neuss: microm GmbH, Neuss.